Closely related to explanations in AVs, recent works have applied deep learning approaches to explain actions in autonomous driving  [1]}, [2]}. For example, Kim et al. [1]} proposed an approach for textual explanation generation for autonomous vehicles' actions through an attention-based video-to-text deep model trained on their BDD-X dataset. The explanations were provided in form of attention maps and texts.
These works are limited in terms of the transparency of the explainer, and the intelligibility of the explanations provided. Some authors advocate for interpretable approaches to explainability in AVs. For instance, Omeiza et al. [4]} proposed a social-technical approach to explainability and proposed an interpretable representation for explanations based on a combination of actions, observations, and road rules. Nahata et al. [5]} also applied an interpretable method to explain risk prediction models for autonomous vehicles.
Other recent research [6]}, [7]}, [8]}, [9]} have explored the relationship between explanations and trust in AVs.
